Article Details

Abstract

With the rapid development of the mobile Internet, the amount of information and information dimensions are growing rapidly. How to set up an information architecture that is convenient for users to find and understand information has become an urgent problem to be optimized. This paper focuses on the positive effect of folksonomy in information classification technology on information architecture. On this basis, this paper uses the characteristics that the Deep Learning algorithm can automatically model the correlation and dependency relationships among multi-labels when dealing with multi-label classification problems, to propose a multi-label classification Deep Learning information architecture supplemented by folksonomy algorithm. It improves the accuracy and relevance of information classification association and provides convenience for users.

Keywords

folksonomy; Deep Learning; information architecture; multi-label classification

References

1. [1] QuestMobile. QuestMobile2023 China Mobile Internet Annual Report: The proportion of users in cities above the second tier increased by 10.5%, and user agglomeration promoted three major changes in Internet use and consumption mode [EB/OL]. (2024-01-30) https://questmobile.com.cn/research/report/1752183696050458625.
2. [2] Tang Chuang Research on Big Data Architecture Design Based on Central Control [C]//The 13th National Conference on Signal and Intelligent Information Processing and Application Hanzhong: [Publisher unknown] May 2019
3. [3] Tang Ronghui Analysis of Computer Information Processing Technology in the Era of Big Data [J]. Network Security Technology and Applications, 2022 (6): 65-67
4. [4] Wu Jiangxing, Zou Hong, Zhang Fan, etc Research on Several Issues Concerning the Development Paradigm of Web3.0 and Network Technology [J]. Science and Technology Review, 2023, 41 (15): 12-21
5. [5] Wang Na, Tian Xiaomeng Research on the Impact and Optimization Strategies of Popular Classification on Information Overload: A Case Study of Douban [J]. Modern Intelligence, 2016, 36 (9): 74-81+87
6. [6] Lu Kun, Zhang Jiayu, Zhang Hongli, etc A review of research on abnormal propagation in social networks [J]. Journal of Communications, 2024, 45 (5): 191-213
7. [7] ROSENFELD L, MORBILLE P, ARANGO J. Information Architecture: Beyond Web Design [M]. Translated by Fan Wangbin and Shi Rong Beijing: Electronic Industry Press, 2016
8. [8] AMEER I，SIDOROV G，GÓMEZ-ADORNO H，et al. Multi-Label Emotion Classification on Code-Mixed Text: Data and Methods [J].IEEE Access，2022，10：8779-8789.
9. [9] HUANG A，XU R，CHEN Y，et al. Research on Multi-Label User Classification of Social Media Based on ML-KNN Algorithm [J/OL].Technological Forecasting and Social Change，2023，188：122271[2024-05-26].https://doi.org/10.1016/j.techfore.2022.122271.
10. [10] GUPTA M，KUMAR P. Recommendation Generation Using Personalized Weight of Meta-Paths in Heterogeneous Information Networks [J].European Journal of Operational Research，2020，284（2）：660-674.
11. [11] Yang Kun Research on Personalized Recommendation Method Based on Machine Learning [D]. Shenyang: Shenyang University of Technology, 2021
12. [12] KANG H，LOU C. AI Agency VS. Human Agency: Understanding Human-AI Interactions on TikTok and Their Implications for User Engagement [J/OL].Journal of Computer-Mediated Communication，2022，27（5）：zmac014 [2024-05-10].https://doi.org/10.1093/jcmc/zmac014.
13. [13] DING Z Y，QIU X P，ZHANG Q. Learning Topical Translation Model for Microblog Hashtag Suggestion [C]//IJCAI'13: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence.Beijing：AAAI Press，2013：2078-2084.
14. [14] NGUYEN T H，GRISHMAN R. Relation Extraction: Perspective from Convolutional Neural Networks [C]//Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing.Denver：Association for Computational Linguistics，2015：39-48.
15. [15] GAO J F，PANTEL P，GAMON M，et al. Modeling Interestingness with Deep Neural Networks [C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP).Doha：Association for Computational Linguistics，2014：2-13.
16. [16] Song Pan Multi label classification method based on label dependency relationship [D]. Beijing: Beijing Jiaotong University, 2019
17. [17] ZHU Y，KWOK J T，ZHOU Z H. Multi-Label Learning with Global and Local Label Correlation [J].IEEE Transactions on Knowledge and Data Engineering，2018，30（6）：1081-1094.
18. [18] DAHIYA K，SAINI D，MITTAL A，et al. DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents [C]//WSDM'21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining.New York：Association for Computing Machinery，2021：31-39.
19. [19] YANG Y Y，ZHOU J Z，LIU J P，et al. Epileptic Seizure Detection Based on Multi-Synchrosqueezing Transform and Multi-label Classification [C]//Proceedings of the 8th International Conference on Signal and Information Processing，Networking and Computers(ICSINC).Ji'nan：Springer，2023，917：1017-1024.
20. [20] CHARTE F，RIVERA A J，JESUS M J，et al. MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation [J].Knowledge-Based Systems，2015，89：385-397.
21. [21] RASTOGI R，KUMAR S. Discriminatory Label-Specific Weights for Multi-label Learning with Missing Labels [J].Neural Process Letters，2023，55（2）：1397-1431.
22. [22] LIN T Y，GOYAL P，GRISHICK R，et al. Focal Loss for Dense Object Detection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，42（2）：318-327.
23. [23] ABBAS A M H，GHAUTH K I，TING C Y. User Experience Design Using Machine Learning: A Systematic Review [J].IEEE Access，2022，10：51501-51514.

Funding

Ministry of Industry and Information Technology's 2022 Industrial Technology Basic Public Service Platform (2022-234-226)

About the authors

Zhang Jingyi (1995-), female, Han ethnicity, from Tangshan, Hebei Province, assistant engineer, bachelor's degree, research direction: information technology, content security, personal information protection; Wang Yuxiao (1991-), female, Han ethnicity, from Baoding, Hebei Province, is an engineer with a master's degree. Her research interests include big data, mobile security, and personal information protection.